An Enhanced Imputation Approach for Spatio-Temporal Clinical Data

Recently, clinical decision making processes largely benefit from electronic health records (EHR) to improve the diagnosis outcomes and quality of patient care. In particular, patients’ conditions in critical care are routinely monitored through essential vital signs such as respiratory rate, heart rate and blood pressure, and lab results. These patient variables in a spatio-temporal and irregular form provide valuable information to support time-dependent decision making. However, the absence of values in these data is almost inevitable due to various causes including irregular sampling rate, sensor dropoff, or manual interference during the data collection process. It, therefore, inhibits the extraction of meaningful information using data-driven machine-learning techniques. To this end, we propose a novel imputation method to enhance data quality. In our approach, we improve the performance of pre-filling methods including mean imputation and interpolations by 7%– 95% on both synthetic dataset and MIMIC III database. Also, our method improves the mean imputation and outperforms the 3D-MICE for 9 out of 13 laboratory variables by 25%–80% on 8267 MIMIC III patients.